knitr::opts_chunk$set(
  echo = FALSE, message = FALSE, warning = FALSE,
  fig.width = 7, fig.height = 4, fig.retina = 2
)
options(knitr.kable.NA = "")

suppressPackageStartupMessages({
  pacman::p_load(dplyr, tidyr, ggplot2, tibble, knitr, kableExtra)
})
root <- ".../projects/abcd-projs/smri-pub-abcd/"
source("longComBat-pub-sMRI-abcd.R")   

log_file <- "render_progress.log"
writeLines("", log_file)

pipelines

thickness — f

[longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects

Harmonization Model: age

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6

dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -11028.53 -11001.73 5518.266
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -11101.46 -11047.86 5558.731 1 vs 2 80.93027 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -9208.909 -9182.108 4608.454
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -9200.964 -9147.363 4608.482 1 vs 2 0.0555966 0.9996207

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 2.813 0.118
brain_metric scanner2 1720 2.845 0.137
brain_metric scanner3 204 2.780 0.118
brain_metric scanner5 1596 2.854 0.115
brain_metric scanner6 2015 2.859 0.119
brain_metric.combat scanner1 469 2.854 0.127
brain_metric.combat scanner2 1720 2.848 0.136
brain_metric.combat scanner3 204 2.844 0.122
brain_metric.combat scanner5 1596 2.842 0.133
brain_metric.combat scanner6 2015 2.850 0.136
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.023
age_slope -0.023
scanner:scanner3 2.783 2.847 0.064 2.297
scanner:scanner1 2.806 2.847 0.041 1.474
scanner:scanner5 2.859 2.847 -0.012 -0.417
scanner:scanner6 2.857 2.848 -0.009 -0.310
scanner:scanner2 2.845 2.848 0.003 0.090
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled

dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -11028.53 -11001.73 5518.266
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -11101.46 -11047.86 5558.731 1 vs 2 80.93027 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -9210.049 -9183.248 4609.024
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -9202.094 -9148.493 4609.047 1 vs 2 0.045162 0.9997489

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 2.813 0.118
brain_metric scanner2 1720 2.845 0.137
brain_metric scanner3 204 2.780 0.118
brain_metric scanner5 1596 2.854 0.115
brain_metric scanner6 2015 2.859 0.119
brain_metric.combat scanner1 469 2.854 0.127
brain_metric.combat scanner2 1720 2.847 0.136
brain_metric.combat scanner3 204 2.843 0.122
brain_metric.combat scanner5 1596 2.842 0.133
brain_metric.combat scanner6 2015 2.850 0.136
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.023
age_slope -0.023
scanner:scanner3 2.783 2.846 0.063 2.268
scanner:scanner1 2.806 2.848 0.042 1.490
scanner:scanner5 2.859 2.847 -0.012 -0.410
scanner:scanner6 2.857 2.848 -0.009 -0.314
scanner:scanner2 2.845 2.848 0.002 0.087
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled

dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -11028.53 -11001.73 5518.266
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -11101.46 -11047.86 5558.731 1 vs 2 80.93027 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -9208.994 -9182.193 4608.497
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -9201.053 -9147.452 4608.527 1 vs 2 0.059926 0.99956

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 2.813 0.118
brain_metric scanner2 1720 2.845 0.137
brain_metric scanner3 204 2.780 0.118
brain_metric scanner5 1596 2.854 0.115
brain_metric scanner6 2015 2.859 0.119
brain_metric.combat scanner1 469 2.854 0.127
brain_metric.combat scanner2 1720 2.848 0.136
brain_metric.combat scanner3 204 2.843 0.122
brain_metric.combat scanner5 1596 2.842 0.133
brain_metric.combat scanner6 2015 2.850 0.136
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.023
age_slope -0.023
scanner:scanner3 2.783 2.846 0.064 2.284
scanner:scanner1 2.806 2.848 0.042 1.484
scanner:scanner5 2.859 2.847 -0.012 -0.417
scanner:scanner6 2.857 2.848 -0.009 -0.310
scanner:scanner2 2.845 2.848 0.003 0.088
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7

dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -11028.53 -11001.73 5518.266
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -11101.46 -11047.86 5558.731 1 vs 2 80.93027 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -9212.788 -9185.987 4610.394
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -9204.859 -9151.258 4610.430 1 vs 2 0.0714682 0.9993765

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 2.813 0.118
brain_metric scanner2 1720 2.845 0.137
brain_metric scanner3 204 2.780 0.118
brain_metric scanner5 1596 2.854 0.115
brain_metric scanner6 2015 2.859 0.119
brain_metric.combat scanner1 469 2.854 0.127
brain_metric.combat scanner2 1720 2.848 0.136
brain_metric.combat scanner3 204 2.843 0.122
brain_metric.combat scanner5 1596 2.842 0.133
brain_metric.combat scanner6 2015 2.850 0.136
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.023
age_slope -0.023
scanner:scanner3 2.783 2.846 0.063 2.266
scanner:scanner1 2.806 2.848 0.042 1.485
scanner:scanner5 2.859 2.847 -0.012 -0.416
scanner:scanner6 2.857 2.848 -0.009 -0.309
scanner:scanner2 2.845 2.848 0.003 0.089
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7

dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -11028.53 -11001.73 5518.266
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -11101.46 -11047.86 5558.731 1 vs 2 80.93027 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -9204.983 -9178.182 4606.491
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -9197.041 -9143.440 4606.521 1 vs 2 0.0587585 0.9995768

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 2.813 0.118
brain_metric scanner2 1720 2.845 0.137
brain_metric scanner3 204 2.780 0.118
brain_metric scanner5 1596 2.854 0.115
brain_metric scanner6 2015 2.859 0.119
brain_metric.combat scanner1 469 2.854 0.127
brain_metric.combat scanner2 1720 2.847 0.136
brain_metric.combat scanner3 204 2.843 0.122
brain_metric.combat scanner5 1596 2.842 0.133
brain_metric.combat scanner6 2015 2.850 0.136
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.023
age_slope -0.023
scanner:scanner3 2.783 2.846 0.064 2.282
scanner:scanner1 2.806 2.848 0.042 1.484
scanner:scanner5 2.859 2.847 -0.012 -0.416
scanner:scanner6 2.857 2.848 -0.009 -0.310
scanner:scanner2 2.845 2.848 0.002 0.088
scanner:scanner7
scanner:scanner8

thickness — m

[longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects

Harmonization Model: age

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6

dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -12255.3 -12227.97 6131.651
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -12363.7 -12309.03 6189.848 1 vs 2 116.3952 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -10226.99 -10199.65 5117.495
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -10219.05 -10164.38 5117.524 1 vs 2 0.0585278 0.9995801

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 2.779 0.132
brain_metric scanner2 1808 2.829 0.144
brain_metric scanner3 196 2.756 0.129
brain_metric scanner5 2016 2.841 0.111
brain_metric scanner6 2264 2.848 0.121
brain_metric.combat scanner1 579 2.833 0.138
brain_metric.combat scanner2 1808 2.835 0.141
brain_metric.combat scanner3 196 2.819 0.133
brain_metric.combat scanner5 2016 2.828 0.131
brain_metric.combat scanner6 2264 2.834 0.137
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.014
age_slope -0.014
scanner:scanner3 2.769 2.829 0.060 2.163
scanner:scanner1 2.777 2.832 0.055 1.975
scanner:scanner6 2.844 2.831 -0.014 -0.477
scanner:scanner5 2.844 2.831 -0.012 -0.432
scanner:scanner2 2.825 2.831 0.007 0.237
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled

dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -12255.3 -12227.97 6131.651
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -12363.7 -12309.03 6189.848 1 vs 2 116.3952 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -10228.03 -10200.69 5118.014
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -10220.18 -10165.51 5118.090 1 vs 2 0.1534112 0.9972043

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 2.779 0.132
brain_metric scanner2 1808 2.829 0.144
brain_metric scanner3 196 2.756 0.129
brain_metric scanner5 2016 2.841 0.111
brain_metric scanner6 2264 2.848 0.121
brain_metric.combat scanner1 579 2.834 0.138
brain_metric.combat scanner2 1808 2.835 0.141
brain_metric.combat scanner3 196 2.817 0.133
brain_metric.combat scanner5 2016 2.828 0.131
brain_metric.combat scanner6 2264 2.834 0.137
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.014
age_slope -0.014
scanner:scanner3 2.769 2.827 0.058 2.110
scanner:scanner1 2.777 2.832 0.056 2.001
scanner:scanner6 2.844 2.831 -0.014 -0.480
scanner:scanner5 2.844 2.831 -0.012 -0.427
scanner:scanner2 2.825 2.831 0.007 0.234
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled

dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -12255.3 -12227.97 6131.651
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -12363.7 -12309.03 6189.848 1 vs 2 116.3952 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -10226.93 -10199.59 5117.465
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -10219.00 -10164.32 5117.498 1 vs 2 0.0666898 0.9994563

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 2.779 0.132
brain_metric scanner2 1808 2.829 0.144
brain_metric scanner3 196 2.756 0.129
brain_metric scanner5 2016 2.841 0.111
brain_metric scanner6 2264 2.848 0.121
brain_metric.combat scanner1 579 2.833 0.138
brain_metric.combat scanner2 1808 2.835 0.141
brain_metric.combat scanner3 196 2.819 0.133
brain_metric.combat scanner5 2016 2.828 0.131
brain_metric.combat scanner6 2264 2.834 0.137
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.014
age_slope -0.014
scanner:scanner3 2.769 2.829 0.060 2.161
scanner:scanner1 2.777 2.832 0.055 1.977
scanner:scanner6 2.844 2.831 -0.014 -0.479
scanner:scanner5 2.844 2.831 -0.012 -0.427
scanner:scanner2 2.825 2.831 0.007 0.234
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7

dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -12255.3 -12227.97 6131.651
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -12363.7 -12309.03 6189.848 1 vs 2 116.3952 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -10228.63 -10201.29 5118.314
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -10220.79 -10166.12 5118.396 1 vs 2 0.163817 0.9968232

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 2.779 0.132
brain_metric scanner2 1808 2.829 0.144
brain_metric scanner3 196 2.756 0.129
brain_metric scanner5 2016 2.841 0.111
brain_metric scanner6 2264 2.848 0.121
brain_metric.combat scanner1 579 2.834 0.138
brain_metric.combat scanner2 1808 2.835 0.141
brain_metric.combat scanner3 196 2.817 0.133
brain_metric.combat scanner5 2016 2.828 0.131
brain_metric.combat scanner6 2264 2.835 0.137
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.014
age_slope -0.014
scanner:scanner3 2.769 2.827 0.058 2.097
scanner:scanner1 2.777 2.833 0.056 2.003
scanner:scanner6 2.844 2.831 -0.013 -0.472
scanner:scanner5 2.844 2.831 -0.012 -0.437
scanner:scanner2 2.825 2.831 0.007 0.236
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7

dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -12255.3 -12227.97 6131.651
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -12363.7 -12309.03 6189.848 1 vs 2 116.3952 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 -10224.97 -10197.63 5116.483
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 -10217.03 -10162.36 5116.513 1 vs 2 0.0608185 0.9995469

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 2.779 0.132
brain_metric scanner2 1808 2.829 0.144
brain_metric scanner3 196 2.756 0.129
brain_metric scanner5 2016 2.841 0.111
brain_metric scanner6 2264 2.848 0.121
brain_metric.combat scanner1 579 2.833 0.138
brain_metric.combat scanner2 1808 2.835 0.141
brain_metric.combat scanner3 196 2.819 0.133
brain_metric.combat scanner5 2016 2.828 0.131
brain_metric.combat scanner6 2264 2.834 0.137
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -0.014
age_slope -0.014
scanner:scanner3 2.769 2.829 0.060 2.166
scanner:scanner1 2.777 2.832 0.055 1.979
scanner:scanner6 2.844 2.831 -0.014 -0.478
scanner:scanner5 2.844 2.831 -0.012 -0.428
scanner:scanner2 2.825 2.831 0.007 0.234
scanner:scanner4
scanner:scanner7
scanner:scanner8

area — f

[longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects

Harmonization Model: age

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6

dataset: area | sex: f | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 82923.34 82950.14 -41457.67
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 82778.44 82832.04 -41381.22 1 vs 2 152.9054 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 84702.09 84728.89 -42347.05
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 84709.40 84763.00 -42346.70 1 vs 2 0.6961797 0.9517992

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 3449.066 402.696
brain_metric scanner2 1720 3640.774 451.854
brain_metric scanner3 204 3349.922 398.007
brain_metric scanner5 1596 3478.284 413.083
brain_metric scanner6 2015 3463.817 412.676
brain_metric.combat scanner1 469 3542.788 422.232
brain_metric.combat scanner2 1720 3509.192 448.405
brain_metric.combat scanner3 204 3508.632 418.113
brain_metric.combat scanner5 1596 3509.699 427.640
brain_metric.combat scanner6 2015 3513.268 437.272
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -11.067
age_slope -6.562
scanner:scanner3 3350.180 3511.397 161.217 4.812
scanner:scanner2 3640.186 3510.750 -129.436 -3.556
scanner:scanner1 3414.452 3504.021 89.568 2.623
scanner:scanner6 3459.880 3512.040 52.160 1.508
scanner:scanner5 3473.810 3502.206 28.396 0.817
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled

dataset: area | sex: f | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 82923.34 82950.14 -41457.67
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 82778.44 82832.04 -41381.22 1 vs 2 152.9054 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 84700.83 84727.64 -42346.42
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 84708.30 84761.90 -42346.15 1 vs 2 0.5327495 0.9702355

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 3449.066 402.696
brain_metric scanner2 1720 3640.774 451.854
brain_metric scanner3 204 3349.922 398.007
brain_metric scanner5 1596 3478.284 413.083
brain_metric scanner6 2015 3463.817 412.676
brain_metric.combat scanner1 469 3545.002 422.183
brain_metric.combat scanner2 1720 3508.670 448.402
brain_metric.combat scanner3 204 3504.084 417.978
brain_metric.combat scanner5 1596 3510.262 427.653
brain_metric.combat scanner6 2015 3513.212 437.232
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -11.067
age_slope -6.506
scanner:scanner3 3350.180 3506.499 156.319 4.666
scanner:scanner2 3640.186 3510.207 -129.978 -3.571
scanner:scanner1 3414.452 3506.530 92.078 2.697
scanner:scanner6 3459.880 3511.809 51.929 1.501
scanner:scanner5 3473.810 3502.994 29.184 0.840
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled

dataset: area | sex: f | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 82923.34 82950.14 -41457.67
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 82778.44 82832.04 -41381.22 1 vs 2 152.9054 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 84702.05 84728.85 -42347.02
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 84709.37 84762.97 -42346.69 1 vs 2 0.6736663 0.9545362

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 3449.066 402.696
brain_metric scanner2 1720 3640.774 451.854
brain_metric scanner3 204 3349.922 398.007
brain_metric scanner5 1596 3478.284 413.083
brain_metric scanner6 2015 3463.817 412.676
brain_metric.combat scanner1 469 3543.958 422.249
brain_metric.combat scanner2 1720 3508.953 448.405
brain_metric.combat scanner3 204 3506.901 418.066
brain_metric.combat scanner5 1596 3509.757 427.639
brain_metric.combat scanner6 2015 3513.328 437.265
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -11.067
age_slope -6.553
scanner:scanner3 3350.180 3509.531 159.351 4.756
scanner:scanner2 3640.186 3510.494 -129.692 -3.563
scanner:scanner1 3414.452 3505.359 90.907 2.662
scanner:scanner6 3459.880 3512.099 52.219 1.509
scanner:scanner5 3473.810 3502.241 28.431 0.818
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7

dataset: area | sex: f | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 82923.34 82950.14 -41457.67
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 82778.44 82832.04 -41381.22 1 vs 2 152.9054 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 84690.86 84717.66 -42341.43
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 84698.18 84751.78 -42341.09 1 vs 2 0.6780178 0.9540118

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 3449.066 402.696
brain_metric scanner2 1720 3640.774 451.854
brain_metric scanner3 204 3349.922 398.007
brain_metric scanner5 1596 3478.284 413.083
brain_metric scanner6 2015 3463.817 412.676
brain_metric.combat scanner1 469 3544.602 422.142
brain_metric.combat scanner2 1720 3508.565 448.352
brain_metric.combat scanner3 204 3502.533 417.301
brain_metric.combat scanner5 1596 3509.773 427.543
brain_metric.combat scanner6 2015 3513.944 437.663
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -11.067
age_slope -6.462
scanner:scanner3 3350.180 3505.712 155.532 4.642
scanner:scanner2 3640.186 3510.124 -130.061 -3.573
scanner:scanner1 3414.452 3505.815 91.363 2.676
scanner:scanner6 3459.880 3512.489 52.610 1.521
scanner:scanner5 3473.810 3502.544 28.734 0.827
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7

dataset: area | sex: f | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 82923.34 82950.14 -41457.67
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 82778.44 82832.04 -41381.22 1 vs 2 152.9054 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 84704.75 84731.55 -42348.37
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 84712.07 84765.67 -42348.03 1 vs 2 0.6797435 0.9538033

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 3449.066 402.696
brain_metric scanner2 1720 3640.774 451.854
brain_metric scanner3 204 3349.922 398.007
brain_metric scanner5 1596 3478.284 413.083
brain_metric scanner6 2015 3463.817 412.676
brain_metric.combat scanner1 469 3544.577 422.383
brain_metric.combat scanner2 1720 3509.098 448.447
brain_metric.combat scanner3 204 3506.549 418.049
brain_metric.combat scanner5 1596 3509.571 427.648
brain_metric.combat scanner6 2015 3513.244 437.249
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope -11.067
age_slope -6.544
scanner:scanner3 3350.180 3509.334 159.153 4.751
scanner:scanner2 3640.186 3510.580 -129.605 -3.560
scanner:scanner1 3414.452 3505.644 91.192 2.671
scanner:scanner6 3459.880 3512.063 52.183 1.508
scanner:scanner5 3473.810 3502.153 28.343 0.816
scanner:scanner7
scanner:scanner8

area — m

[longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects

Harmonization Model: age

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6

dataset: area | sex: m | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 95185.14 95212.48 -47588.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 94902.78 94957.45 -47443.39 1 vs 2 290.3618 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 97129.24 97156.58 -48560.62
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 97137.02 97191.69 -48560.51 1 vs 2 0.2276452 0.9939934

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 3755.839 462.895
brain_metric scanner2 1808 4037.319 486.071
brain_metric scanner3 196 3646.485 514.494
brain_metric scanner5 2016 3792.857 445.779
brain_metric scanner6 2264 3833.975 449.369
brain_metric.combat scanner1 579 3880.490 477.411
brain_metric.combat scanner2 1808 3851.263 479.856
brain_metric.combat scanner3 196 3866.100 526.370
brain_metric.combat scanner5 2016 3843.197 467.669
brain_metric.combat scanner6 2264 3886.699 469.732
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 6.835
age_slope 8.893
scanner:scanner3 3646.649 3878.281 231.632 6.352
scanner:scanner2 4051.359 3860.821 -190.538 -4.703
scanner:scanner1 3737.765 3864.961 127.196 3.403
scanner:scanner6 3812.994 3866.440 53.446 1.402
scanner:scanner5 3811.261 3863.904 52.644 1.381
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled

dataset: area | sex: m | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 95185.14 95212.48 -47588.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 94902.78 94957.45 -47443.39 1 vs 2 290.3618 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 97128.27 97155.60 -48560.13
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 97136.11 97190.78 -48560.05 1 vs 2 0.1589453 0.9970045

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 3755.839 462.895
brain_metric scanner2 1808 4037.319 486.071
brain_metric scanner3 196 3646.485 514.494
brain_metric scanner5 2016 3792.857 445.779
brain_metric scanner6 2264 3833.975 449.369
brain_metric.combat scanner1 579 3883.229 477.432
brain_metric.combat scanner2 1808 3850.960 479.852
brain_metric.combat scanner3 196 3860.053 526.315
brain_metric.combat scanner5 2016 3843.420 467.625
brain_metric.combat scanner6 2264 3886.566 469.715
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 6.835
age_slope 8.915
scanner:scanner3 3646.649 3871.430 224.780 6.164
scanner:scanner2 4051.359 3860.500 -190.859 -4.711
scanner:scanner1 3737.765 3868.014 130.249 3.485
scanner:scanner6 3812.994 3866.167 53.173 1.395
scanner:scanner5 3811.261 3864.317 53.056 1.392
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled

dataset: area | sex: m | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 95185.14 95212.48 -47588.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 94902.78 94957.45 -47443.39 1 vs 2 290.3618 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 97129.20 97156.54 -48560.60
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 97136.98 97191.65 -48560.49 1 vs 2 0.2211063 0.9943213

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 3755.839 462.895
brain_metric scanner2 1808 4037.319 486.071
brain_metric scanner3 196 3646.485 514.494
brain_metric scanner5 2016 3792.857 445.779
brain_metric scanner6 2264 3833.975 449.369
brain_metric.combat scanner1 579 3880.744 477.413
brain_metric.combat scanner2 1808 3850.934 479.853
brain_metric.combat scanner3 196 3865.860 526.351
brain_metric.combat scanner5 2016 3843.452 467.656
brain_metric.combat scanner6 2264 3886.691 469.716
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 6.835
age_slope 8.899
scanner:scanner3 3646.649 3878.021 231.372 6.345
scanner:scanner2 4051.359 3860.472 -190.888 -4.712
scanner:scanner1 3737.765 3865.228 127.462 3.410
scanner:scanner6 3812.994 3866.326 53.332 1.399
scanner:scanner5 3811.261 3864.307 53.046 1.392
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7

dataset: area | sex: m | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 95185.14 95212.48 -47588.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 94902.78 94957.45 -47443.39 1 vs 2 290.3618 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 97110.10 97137.43 -48551.05
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 97117.86 97172.53 -48550.93 1 vs 2 0.2442297 0.9931241

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 3755.839 462.895
brain_metric scanner2 1808 4037.319 486.071
brain_metric scanner3 196 3646.485 514.494
brain_metric scanner5 2016 3792.857 445.779
brain_metric scanner6 2264 3833.975 449.369
brain_metric.combat scanner1 579 3883.105 477.438
brain_metric.combat scanner2 1808 3850.890 479.864
brain_metric.combat scanner3 196 3859.114 526.563
brain_metric.combat scanner5 2016 3842.187 467.359
brain_metric.combat scanner6 2264 3887.836 469.788
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 6.835
age_slope 8.892
scanner:scanner3 3646.649 3871.565 224.916 6.168
scanner:scanner2 4051.359 3860.576 -190.783 -4.709
scanner:scanner1 3737.765 3867.730 129.964 3.477
scanner:scanner6 3812.994 3867.089 54.095 1.419
scanner:scanner5 3811.261 3863.281 52.020 1.365
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7

dataset: area | sex: m | region: smri_area_cdk_mdtmlh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 95185.14 95212.48 -47588.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 94902.78 94957.45 -47443.39 1 vs 2 290.3618 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 97129.59 97156.92 -48560.79
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 97137.36 97192.03 -48560.68 1 vs 2 0.2306247 0.9938412

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 3755.839 462.895
brain_metric scanner2 1808 4037.319 486.071
brain_metric scanner3 196 3646.485 514.494
brain_metric scanner5 2016 3792.857 445.779
brain_metric scanner6 2264 3833.975 449.369
brain_metric.combat scanner1 579 3880.444 477.540
brain_metric.combat scanner2 1808 3850.944 479.892
brain_metric.combat scanner3 196 3866.880 526.347
brain_metric.combat scanner5 2016 3843.384 467.621
brain_metric.combat scanner6 2264 3886.732 469.679
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 6.835
age_slope 8.888
scanner:scanner3 3646.649 3878.740 232.091 6.365
scanner:scanner2 4051.359 3860.543 -190.816 -4.710
scanner:scanner1 3737.765 3865.058 127.292 3.406
scanner:scanner6 3812.994 3866.321 53.328 1.399
scanner:scanner5 3811.261 3864.217 52.956 1.389
scanner:scanner4
scanner:scanner7
scanner:scanner8

volume — f

## longcombat is dropping 2 feature(s) with NA.

[longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects

Harmonization Model: age

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6

dataset: volume | sex: f | region: smri_vol_scs_amygdalarh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 75803.15 75829.95 -37897.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 75746.13 75799.73 -37865.07 1 vs 2 65.01632 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 77717.92 77744.72 -38854.96
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 77725.84 77779.45 -38854.92 1 vs 2 0.0717721 0.9993713

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 1792.106 188.323
brain_metric scanner2 1720 1726.334 191.118
brain_metric scanner3 204 1781.268 183.449
brain_metric scanner5 1596 1782.956 188.630
brain_metric scanner6 2015 1775.741 188.518
brain_metric.combat scanner1 469 1772.467 197.977
brain_metric.combat scanner2 1720 1767.412 200.762
brain_metric.combat scanner3 204 1765.441 190.134
brain_metric.combat scanner5 1596 1760.986 198.590
brain_metric.combat scanner6 2015 1764.321 198.678
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 8.034
age_slope 8.112
scanner:scanner2 1723.371 1764.659 41.288 2.396
scanner:scanner5 1785.131 1763.086 -22.045 -1.235
scanner:scanner1 1783.755 1766.018 -17.736 -0.994
scanner:scanner3 1780.944 1765.092 -15.852 -0.890
scanner:scanner6 1776.311 1764.380 -11.931 -0.672
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled

dataset: volume | sex: f | region: smri_vol_scs_amygdalarh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 75803.15 75829.95 -37897.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 75746.13 75799.73 -37865.07 1 vs 2 65.01632 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 77717.62 77744.42 -38854.81
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 77725.55 77779.15 -38854.77 1 vs 2 0.0698744 0.9994037

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 1792.106 188.323
brain_metric scanner2 1720 1726.334 191.118
brain_metric scanner3 204 1781.268 183.449
brain_metric scanner5 1596 1782.956 188.630
brain_metric scanner6 2015 1775.741 188.518
brain_metric.combat scanner1 469 1773.020 197.982
brain_metric.combat scanner2 1720 1767.292 200.756
brain_metric.combat scanner3 204 1764.442 190.136
brain_metric.combat scanner5 1596 1761.164 198.585
brain_metric.combat scanner6 2015 1764.255 198.678
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 8.034
age_slope 8.125
scanner:scanner2 1723.371 1764.540 41.170 2.389
scanner:scanner5 1785.131 1763.285 -21.846 -1.224
scanner:scanner1 1783.755 1766.584 -17.170 -0.963
scanner:scanner3 1780.944 1764.081 -16.863 -0.947
scanner:scanner6 1776.311 1764.301 -12.010 -0.676
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled

dataset: volume | sex: f | region: smri_vol_scs_amygdalarh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 75803.15 75829.95 -37897.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 75746.13 75799.73 -37865.07 1 vs 2 65.01632 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 77717.43 77744.23 -38854.71
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 77725.33 77778.93 -38854.67 1 vs 2 0.0946972 0.9989138

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 1792.106 188.323
brain_metric scanner2 1720 1726.334 191.118
brain_metric scanner3 204 1781.268 183.449
brain_metric scanner5 1596 1782.956 188.630
brain_metric scanner6 2015 1775.741 188.518
brain_metric.combat scanner1 469 1773.329 197.979
brain_metric.combat scanner2 1720 1767.248 200.755
brain_metric.combat scanner3 204 1764.267 190.132
brain_metric.combat scanner5 1596 1761.015 198.579
brain_metric.combat scanner6 2015 1764.356 198.682
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 8.034
age_slope 8.119
scanner:scanner2 1723.371 1764.496 41.125 2.386
scanner:scanner5 1785.131 1763.112 -22.019 -1.233
scanner:scanner3 1780.944 1763.903 -17.041 -0.957
scanner:scanner1 1783.755 1766.906 -16.849 -0.945
scanner:scanner6 1776.311 1764.413 -11.898 -0.670
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7

dataset: volume | sex: f | region: smri_vol_scs_amygdalarh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 75803.15 75829.95 -37897.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 75746.13 75799.73 -37865.07 1 vs 2 65.01632 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 77708.42 77735.22 -38850.21
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 77716.31 77769.91 -38850.16 1 vs 2 0.1074109 0.9986085

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 1792.106 188.323
brain_metric scanner2 1720 1726.334 191.118
brain_metric scanner3 204 1781.268 183.449
brain_metric scanner5 1596 1782.956 188.630
brain_metric scanner6 2015 1775.741 188.518
brain_metric.combat scanner1 469 1772.498 197.869
brain_metric.combat scanner2 1720 1767.195 200.603
brain_metric.combat scanner3 204 1764.090 190.516
brain_metric.combat scanner5 1596 1760.712 198.598
brain_metric.combat scanner6 2015 1764.851 198.622
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 8.034
age_slope 8.107
scanner:scanner2 1723.371 1764.450 41.079 2.384
scanner:scanner5 1785.131 1762.874 -22.257 -1.247
scanner:scanner1 1783.755 1766.097 -17.658 -0.990
scanner:scanner3 1780.944 1763.712 -17.231 -0.968
scanner:scanner6 1776.311 1764.849 -11.462 -0.645
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7

dataset: volume | sex: f | region: smri_vol_scs_amygdalarh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 75803.15 75829.95 -37897.57
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 75746.13 75799.73 -37865.07 1 vs 2 65.01632 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 77722.00 77748.81 -38857.00
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 77729.91 77783.51 -38856.95 1 vs 2 0.0968784 0.998864

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 469 1792.106 188.323
brain_metric scanner2 1720 1726.334 191.118
brain_metric scanner3 204 1781.268 183.449
brain_metric scanner5 1596 1782.956 188.630
brain_metric scanner6 2015 1775.741 188.518
brain_metric.combat scanner1 469 1773.401 198.054
brain_metric.combat scanner2 1720 1767.257 200.774
brain_metric.combat scanner3 204 1764.213 190.099
brain_metric.combat scanner5 1596 1761.009 198.602
brain_metric.combat scanner6 2015 1764.341 198.732
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 8.034
age_slope 8.120
scanner:scanner2 1723.371 1764.508 41.137 2.387
scanner:scanner5 1785.131 1763.110 -22.021 -1.234
scanner:scanner3 1780.944 1763.856 -17.088 -0.960
scanner:scanner1 1783.755 1766.966 -16.788 -0.941
scanner:scanner6 1776.311 1764.395 -11.916 -0.671
scanner:scanner7
scanner:scanner8

volume — m

## longcombat is dropping 2 feature(s) with NA.

[longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects

Harmonization Model: age

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6

dataset: volume | sex: m | region: smri_vol_scs_amygdalarh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 87765.12 87792.46 -43878.56
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 87676.81 87731.48 -43830.40 1 vs 2 96.31319 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 89945.34 89972.68 -44968.67
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 89953.18 90007.85 -44968.59 1 vs 2 0.1670111 0.9967016

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 1957.793 226.961
brain_metric scanner2 1808 1887.890 210.181
brain_metric scanner3 196 1924.909 208.734
brain_metric scanner5 2016 1951.739 206.193
brain_metric scanner6 2264 1953.379 211.401
brain_metric.combat scanner1 579 1926.908 234.271
brain_metric.combat scanner2 1808 1940.706 223.463
brain_metric.combat scanner3 196 1944.029 215.666
brain_metric.combat scanner5 2016 1928.233 215.624
brain_metric.combat scanner6 2264 1938.445 221.035
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 21.828
age_slope 21.880
scanner:scanner2 1881.675 1935.396 53.721 2.855
scanner:scanner1 1965.306 1932.062 -33.243 -1.692
scanner:scanner5 1957.986 1933.281 -24.705 -1.262
scanner:scanner3 1914.673 1934.310 19.637 1.026
scanner:scanner6 1949.493 1935.556 -13.938 -0.715
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled

dataset: volume | sex: m | region: smri_vol_scs_amygdalarh

Distributions

Harmonization Model: age_tempo

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled

dataset: volume | sex: m | region: smri_vol_scs_amygdalarh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 87765.12 87792.46 -43878.56
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 87676.81 87731.48 -43830.40 1 vs 2 96.31319 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 89945.36 89972.69 -44968.68
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 89953.22 90007.89 -44968.61 1 vs 2 0.1416313 0.9976079

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 1957.793 226.961
brain_metric scanner2 1808 1887.890 210.181
brain_metric scanner3 196 1924.909 208.734
brain_metric scanner5 2016 1951.739 206.193
brain_metric scanner6 2264 1953.379 211.401
brain_metric.combat scanner1 579 1926.996 234.273
brain_metric.combat scanner2 1808 1940.587 223.461
brain_metric.combat scanner3 196 1943.951 215.669
brain_metric.combat scanner5 2016 1928.369 215.626
brain_metric.combat scanner6 2264 1938.403 221.032
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 21.828
age_slope 21.882
scanner:scanner2 1881.675 1935.275 53.600 2.849
scanner:scanner1 1965.306 1932.151 -33.155 -1.687
scanner:scanner5 1957.986 1933.432 -24.554 -1.254
scanner:scanner3 1914.673 1934.237 19.564 1.022
scanner:scanner6 1949.493 1935.505 -13.989 -0.718
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_timing_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7

dataset: volume | sex: m | region: smri_vol_scs_amygdalarh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 87765.12 87792.46 -43878.56
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 87676.81 87731.48 -43830.40 1 vs 2 96.31319 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 89945.38 89972.71 -44968.69
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 89953.18 90007.85 -44968.59 1 vs 2 0.1991848 0.995358

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 1957.793 226.961
brain_metric scanner2 1808 1887.890 210.181
brain_metric scanner3 196 1924.909 208.734
brain_metric scanner5 2016 1951.739 206.193
brain_metric scanner6 2264 1953.379 211.401
brain_metric.combat scanner1 579 1927.357 234.012
brain_metric.combat scanner2 1808 1940.684 223.418
brain_metric.combat scanner3 196 1942.564 216.057
brain_metric.combat scanner5 2016 1928.022 215.698
brain_metric.combat scanner6 2264 1938.660 221.047
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 21.828
age_slope 21.871
scanner:scanner2 1881.675 1935.395 53.720 2.855
scanner:scanner1 1965.306 1932.469 -32.836 -1.671
scanner:scanner5 1957.986 1933.091 -24.895 -1.271
scanner:scanner3 1914.673 1932.895 18.222 0.952
scanner:scanner6 1949.493 1935.743 -13.751 -0.705
scanner:scanner4
scanner:scanner7
scanner:scanner8

Harmonization Model: age_tempo_interaction

longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7

dataset: volume | sex: m | region: smri_vol_scs_amygdalarh

Distributions

Scanner LRTs

LRT before harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0 lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 87765.12 87792.46 -43878.56
m1 lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 87676.81 87731.48 -43830.40 1 vs 2 96.31319 0
LRT after harmonization
call Model df AIC BIC logLik Test L.Ratio p-value
m0c lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 1 4 89947.24 89974.58 -44969.62
m1c lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 &#124; id, method = “ML”, na.action = na.omit) 2 8 89955.08 90009.75 -44969.54 1 vs 2 0.1606397 0.996942

QC Plots and Summaries

ROI summary by scanner (before vs after)
pipeline scanner n mean sd
brain_metric scanner1 579 1957.793 226.961
brain_metric scanner2 1808 1887.890 210.181
brain_metric scanner3 196 1924.909 208.734
brain_metric scanner5 2016 1951.739 206.193
brain_metric scanner6 2264 1953.379 211.401
brain_metric.combat scanner1 579 1927.115 234.325
brain_metric.combat scanner2 1808 1940.618 223.493
brain_metric.combat scanner3 196 1943.393 215.638
brain_metric.combat scanner5 2016 1928.239 215.659
brain_metric.combat scanner6 2264 1938.511 221.006
Fixed effects change (pre vs post)
kind Value_pre Value_post diff pctchg
age_slope 21.828
age_slope 21.885
scanner:scanner2 1881.675 1935.280 53.605 2.849
scanner:scanner1 1965.306 1932.279 -33.027 -1.680
scanner:scanner5 1957.986 1933.311 -24.675 -1.260
scanner:scanner3 1914.673 1933.715 19.043 0.995
scanner:scanner6 1949.493 1935.621 -13.873 -0.712
scanner:scanner4
scanner:scanner7
scanner:scanner8